📚 Learning Hub

Learn & Grow

Interactive modules on blockchain, AI, and Web3

How LLMs Actually Work

Demystify large language models — tokens, transformers, attention mechanisms, and why models sometimes 'hallucinate'.

Master the art of prompting — system prompts, few-shot examples, chain-of-thought reasoning, and structured outputs.

Build AI systems that ground responses in your data — embeddings, vector databases, chunking strategies, and retrieval optimization.

Know when to use each approach — understand the costs, capabilities, and appropriate use cases for fine-tuning, RAG, and advanced prompting.

Build AI systems that take action — function calling, ReAct loops, planning agents, and understanding where agents fail.

Understand the emerging standard for connecting AI models to tools and data sources — how MCP works and why it matters.

Go beyond text — understand how modern AI handles images, audio, and documents with unified models.

Move beyond vibes — learn to systematically evaluate AI outputs using benchmarks, eval frameworks, and human review.

Compare the trade-offs between open models (Llama, Mistral, DeepSeek) and closed models (Claude, GPT, Gemini) — cost, control, and capability.

Understand essential AI safety concepts — alignment, prompt injection, jailbreaks, and responsible deployment practices.